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Coffee quality, which ultimately is reflected in the beverage aroma, relies on several aspects requiring multiple approaches to check it, which can be expensive and/or time-consuming. Therefore, this study aimed to develop and calibrate an electronic nose (e-nose) coupled with chemometrics to approach coffee-related quality tasks. Twelve different metal oxide sensors were employed in the e-nose construction. The tasks were (i) the separation of Coffea arabica and Coffea canephora species, (ii) the distinction between roasting profiles (light, medium, and dark), and (iii) the separation of expired and non-expired coffees. Exploratory analysis with principal component analysis (PCA) pointed to a fair grouping of the tested samples according to their specification, indicating the potential of the volatiles in grouping the samples. Moreover, a supervised classification employing soft independent modeling of class analogies (SIMCA), partial least squares discriminant analysis (PLS-DA), and least squares support vector machine (LS-SVM) led to great results with accuracy above 90% for every task. The performance of each model varies with the specific task, except for the LS-SVM models, which presented a perfect classification for all tasks. Therefore, combining the e-nose with distinct classification models could be used for multiple-purpose classification tasks for producers as a low-cost, rapid, and effective alternative for quality assurance.

Coffea stenophylla is a rare Coffea species boasting a flavor profile comparable to Arabica coffee (Coffea arabica) and has a good adaptability to lowland tropical climates. This species faces increasing threats from climate change, deforestation, and habitat fragmentation in its West African homeland. Using 1037 novel SNP markers derived from Genotyping-by-Sequencing (GBS), we revealed the presence of three distinct natural populations (mean Fst = 0.176) in Sierra Leone. Evidence of recent bottlenecks and small effective population size (118–140) was found across all three populations, reflecting the impact of recent anthropogenic disturbances on this species. Using a model-flexible inference approach, we unveiled a strong ancient bottleneck approximately 23,000 years ago, coinciding with the last glacial maximum (LGM), followed by post-glacial expansion and divergence into distinct genetic clusters. A comparative analysis between ex situ genebanks and natural populations detected a significant gap in genetic diversity, with two out of three natural populations missing from the ex situ genebank collection. These findings highlight the urgent need to improve conservation practices for C. stenophylla in Sierra Leone. The novel SNP markers developed in this study provided valuable tools to support future efforts in conservation and utilization of C. stenophylla genetic resources in West Africa.

Research into microbial interactions during coffee processing is essential for developing new methods that adapt to climate change and improve flavor, thus enhancing the resilience and quality of global coffee production. This study aimed to investigate how microbial communities interact and contribute to flavor development in coffee processing within humid subtropical climates. Employing Illumina sequencing for microbial dynamics analysis, and high-performance liquid chromatography (HPLC) integrated with gas chromatography–mass spectrometry (GC-MS) for metabolite assessment, the study revealed intricate microbial diversity and associated metabolic activities. Throughout the fermentation process, dominant microbial species included Enterobacter, Erwinia, Kluyvera, and Pantoea from the prokaryotic group, and Fusarium, Cladosporium, Kurtzmaniella, Leptosphaerulina, Neonectria, and Penicillium from the eukaryotic group. The key metabolites identified were ethanol, and lactic, acetic, and citric acids. Notably, the bacterial community plays a crucial role in flavor development by utilizing metabolic versatility to produce esters and alcohols, while plant-derived metabolites such as caffeine and linalool remain stable throughout the fermentation process. The undirected network analysis revealed 321 interactions among microbial species and key substances during the fermentation process, with Enterobacter, Kluyvera, and Serratia showing strong connections with sugar and various volatile compounds, such as hexanal, benzaldehyde, 3-methylbenzaldehyde, 2-butenal, and 4-heptenal. These interactions, including inhibitory effects by Fusarium and Cladosporium, suggest microbial adaptability to subtropical conditions, potentially influencing fermentation and coffee quality. The sensory analysis showed that the final beverage obtained a score of 80.83 ± 0.39, being classified as a specialty coffee by the Specialty Coffee Association (SCA) metrics. Nonetheless, further enhancements in acidity, body, and aftertaste could lead to a more balanced flavor profile. The findings of this research hold substantial implications for the coffee industry in humid subtropical regions, offering potential strategies to enhance flavor quality and consistency through controlled fermentation practices. Furthermore, this study contributes to the broader understanding of how microbial ecology interplays with environmental factors to influence food and beverage fermentation, a topic of growing interest in the context of climate change and sustainable agriculture.

The coffee fruit is preferably harvested at the cherry stage, with high moisture and metabolic activity, and must then undergo a drying process for better preservation of the bean and its sensory attributes. In this context, this study aimed to characterize the final quality of the Arara cultivar Arabica coffee processed using the wet method and subjected to six drying methods: three conducted at the agro-industrial establishment (fixed-bed dryer, rotary drum dryer, and combined drying) and three laboratory-scale methods (convective oven, cast-tape drying, and suspended terrace). Drying was carried out to reduce the coffee’s moisture content from an initial value of 46.2% on a wet basis (w.b.) to a final average value of 11.35% (w.b.). The fruits of in natura demucilaged coffee and the processed dry coffee beans were characterized for moisture, ash content, nitrogen compounds, lipids, total titratable acidity, organic acids, sugars, and the instrumental color of the beans. The sensory profile of the Arabica coffee was evaluated by five coffee specialists using the methodology proposed by the Specialty Coffee Association (SCA), and all the coffees were classified as a specialty.

Specialty coffees from various geographical origins were processed using different extraction methods. Four extraction techniques were employed: cold brew (CB), espresso (ES), French press (FR), and aeropress (AE). The potential health benefits of coffee brews were linked to their antioxidant activity, as determined by the DPPH assay, and total polyphenol content (TPC) measured through the Folin–Ciocalteu reducing-capacity assay. The Columbia (C) espresso coffee type (omni-roasting) exhibited the highest antioxidant activity (86.31 ± 0.70) μmol/100 mL, with a TPC value of (44.41 ± 0.35) mg GAE/g. Quantitative analyses of caffeine and chlorogenic acid were conducted using high-performance liquid chromatography (HPLC). The evaluation of coffee aroma profiles involved the application of headspace solid-phase microextraction/gas chromatography–mass spectrometry (HS-SPME/GC-MS) and was complemented by sensory analysis following the Specialty Coffee Association (SCA) standard protocol. The predominant volatile compounds found in all samples included furans, phenols, pyrazines, and terpenes. The EY espresso type (medium dark roasting) had the highest levels of most coffee volatiles. The C cold brew type (omni-roasting) was rated as the preferred coffee in terms of its sensory characteristics and flavour. In summary, ES and CB were found to be more effective extraction methods for the parameters assessed.

An untargeted gas chromatography-mass spectrometry (GC-MS) approach combined with sensory analysis was used to present the effects of different extraction methods (i.e., Pure Brew, V60, AeroPress, and French Press) on specialty graded Coffea arabica from Kenya. Partial Least Square Discriminant analysis and hierarchical clustering were applied as multivariate statistical tools in data analysis. The results showed good discrimination and a clear clustering of the groups of samples based on their volatile profiles. Similarities were found related to the filter material and shape used for the extraction. Samples extracted with paper filters (V60 and AeroPress) resulted in higher percentages of caramel-, and flowery-related compounds, while from metal filter samples (Pure Brew and French Press), more fruity and roasted coffees were obtained. Discriminant analysis allowed the identification of eight compounds with a high VIP (variable important in projection) discriminant value (i.e., >1), with 2-furanmethanol being the main feature in discrimination. Sensorial analyses were carried out through an expert panel test. The main evaluations revealed the French Press system as the lowest-scored sample in all the evaluated parameters, except for acidity, where its score was similar to V60. In conclusion, the data obtained from GC-MS analyses were in line with the sensorial results, confirming that the extraction process plays a fundamental role in the flavor profile of filter coffee beverages.

The aroma of coffee is a complex mixture of more than 1000 compounds. The volatile compounds in green and roasted coffee were analyzed to detect several features related to quality, roasting level, origins, and the presence of specific defects. With respect to specialty coffee, the flavor profile and peculiarities of the aforementioned characteristics are even more relevant knowing the expectations of consumers to find, in a cup of coffee, unicity bestowed by its origin and post-harvesting processes. In this work, which dealt with 46 lots of specialty Arabica coffee, we used HS-SPME/GC–MS to detect the volatile compounds in green coffees together with those in the same coffees roasted at three different levels to identify whether differences in headspace composition were ascribable to the origin, the post-harvesting processes, and the roasting profiles. The main results are related to the discriminant power of the volatile compounds in green coffee, which are impacted by the origins more than the post-harvesting processes. Compounds such as linalool and 2,3-butanediol were more concentrated in natural coffees, while hexanal was more concentrated in washed varieties (p < 0.05). In roasted coffees, the differences in composition were due to roasting levels, countries of origin, and the post-harvesting processes, in descending order of significance.

In this study, an investigation of the microbial community structure and chemical changes in different layers of a static coffee beans fermentation tank (named self-induced anaerobic fermentation—SIAF) was conducted at different times (24, 48, and 72 h). The microbial taxonomic composition comprised a high prevalence of Enterobacteriaceae and Nectriaceae and low prevalence of lactic acid bacteria and yeast, which greatly differs from the traditional process performed in open tanks. No major variation in bacterial and fungal diversity was observed between the bottom, middle, and top layers of the fermentation tank. On the other hand, the metabolism of these microorganisms varied significantly, showing a higher consumption of pulp sugar and production of metabolites in the bottom and middle layers compared to the top part of the fermentation tank. Extended processes (48 and 72 h) allowed a higher production of key-metabolites during fermentation (e.g., 3-octanol, ethyl acetate, and amyl acetate), accumulation in roasted coffee beans (acetic acid, pyrazine, methyl, 2-propanone, 1-hydroxy), and diversification of sensory profiles of coffee beverages compared to 24 h of fermentation process. In summary, this study demonstrated that SIAF harbored radically different dominant microbial groups compared to traditional coffee processing, and diversification of fermentation time could be an important tool to provide coffee beverages with novel and desirable flavor profiles.

C. canephora (syn. C. robusta) is distinctive due to its rising industrial value and pathogen resistance. Both altitude and post-harvest methods influence coffee cup quality; however, modest information is known about this coffee species. Therefore, the aim of this study was to determine the relationship between four different altitudes and post-harvest processes (dry, honey, and wet) to the improvement of the organoleptic quality of the C. canephora congolensis and conilon drink. For dry processing, congolensis and conilon showed the lowest scores in terms of fragrance/aroma, flavour, aftertaste, salt–acid, bitter–sweet, and body. Above 625 m, coffees from dry, honey, and wet processes increased scores in their sensory attributes, but there was no difference at such high altitudes when comparing post-harvest samples. Dry-processed coffee samples had total scores over 80 points at high altitudes. Conilon was perceived to have the best sensory attributes at high altitudes using honey processing. In general, the wet-processed congolensis and conilon samples had a tastier profile than dry-processed ones.

The Specialty Coffee Association (SCA) sensory analysis protocol is the methodology that is used to classify specialty coffees. However, because the sensory analysis is sensitive to the taster’s training, cognitive psychology, and physiology, among other parameters, the feasibility of instrumental approaches has been recently studied for complementing such analyses. Spectroscopic methods, mainly near infrared (NIR) and mid infrared (FTIR—Fourier Transform Infrared), have been extensively employed for food quality authentication. In view of the aforementioned, we compared NIR and FTIR to distinguish different qualities and sensory characteristics of specialty coffee samples in the present study. Twenty-eight green coffee beans samples were roasted (in duplicate), with roasting conditions following the SCA protocol for sensory analysis. FTIR and NIR were used to analyze the ground and roasted coffee samples, and the data then submitted to statistical analysis to build up PLS models in order to confirm the quality classifications. The PLS models provided good predictability and classification of the samples. The models were able to accurately predict the scores of specialty coffees. In addition, the NIR spectra provided relevant information on chemical bonds that define specialty coffee in association with sensory aspects, such as the cleanliness of the beverage.

The aim of this study was to determine the effect of roasting on the contents of polyphenols (PPH), acrylamide (AA), and caffeine (CAF) and to analyze heavy metals in specialty coffee beans from Colombia (COL) and Nicaragua (NIC). Samples of NIC were naturally processed and COL was fermented anaerobically. Green beans from COL (COL-GR) and NIC (NIC-GR) were roasted at two levels, light roasting (COL-LIGHT and NIC-LIGHT) and darker roasting (COL-DARK and NIC-DARK), at final temperatures of 210 °C (10 min) and 215 °C (12 min), respectively. Quantitative analyses of PPH identified caffeoylquinic acids (CQA), feruloylquinic acids, and dicaffeoylquinic acids. Isomer 5-CQA was present at the highest levels and reached 60.8 and 57.7% in COL-GR and NIC-GR, 23.4 and 29.3% in COL-LIGHT and NIC-LIGHT, and 18 and 24.2% in COL-DARK and NIC-DARK, respectively, of the total PPH. The total PPH contents were highest in COL-GR (59.76 mg/g dry matter, DM). Roasting affected the contents of PPH, CAF, and AA (p < 0.001, p < 0.011 and p < 0.001, respectively). Nickel and cadmium contents were significantly higher in the COL-GR than in the NIC-GR beans. Darker roasting decreased AA content, but light roasting maintained similar amounts of CAF and total PPH.

The specialty coffee industry is growing and, as a result, there is an accelerated interest in modulating roast profiles to present customers with new and diverse sensory experiences. The present study investigates the chemical and sensory effects of subtle variations in the ‘development time’ phase of the coffee roasting process. Four roast profiles were studied through sensory descriptive analysis (DA), gas chromatography–mass spectrometry (GC–MS), and nuclear magnetic resonance (NMR). Multivariate analysis showed clear separation of DA, GC–MS, and NMR data. A prolonged development time facilitated a statistically significant shift in the chemical and sensory profile of the coffee. The findings suggest that a short development time increases the fruity, sweet and acidic characteristics of the coffee, whereas a longer development time shifts the balance towards a more roasty, nutty, and bitter profile. The results provide evidence that supports the effect of subtle roast profile modulations. This lays a strong foundation for the inclusion of development time as a critical control parameter in the certification system of the Specialty Coffee Association, quality control, and product development strategies.